High volumes of data are captured, stored, and available for use in various types of decision-making. However, it is often difficult or impossible for human users of such data to interpret and apply the data, and to engineer computers to operate based on the data and in a manner that optimizes use of the available data.
Computers are often used in various types of scheduling operations, and many such scheduling operations are straightforward. In some contexts, however, it is still difficult or impossible to make large-scale, accurate, and/or timely scheduling decisions, particularly when certain scheduling constraints exist, and/or when a large number of scheduling variables are present.
For example, some scheduling data relates to machine maintenance, such as in a manufacturing or other type of production facility. There may be many machines in such a facility, such as machines for moving, assembling, painting, or otherwise manipulating goods being produced. Such machines (and individual components thereof) may have differing maintenance schedules, and may have differing maintenance aspects. If machine maintenance is scheduled poorly, unacceptable downtime of the machines (and associated production activities) may be experienced in some cases, as well as additional costs for maintaining, upgrading, or replacing machines or machine components.